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Practical MongoDB Aggregations

Practical MongoDB Aggregations

By : Paul Done
5 (17)
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Practical MongoDB Aggregations

Practical MongoDB Aggregations

5 (17)
By: Paul Done

Overview of this book

Officially endorsed by MongoDB, Inc., Practical MongoDB Aggregations helps you unlock the full potential of the MongoDB aggregation framework, including the latest features of MongoDB 7.0. This book provides practical, easy-to-digest principles and approaches for increasing your effectiveness in developing aggregation pipelines, supported by examples for building pipelines to solve complex data manipulation and analytical tasks. This book is customized for developers, architects, data analysts, data engineers, and data scientists with some familiarity with the aggregation framework. It begins by explaining the framework's architecture and then shows you how to build pipelines optimized for productivity and scale. Given the critical role arrays play in MongoDB's document model, the book delves into best practices for optimally manipulating arrays. The latter part of the book equips you with examples to solve common data processing challenges so you can apply the lessons you've learned to practical situations. By the end of this MongoDB book, you’ll have learned how to utilize the MongoDB aggregation framework to streamline your data analysis and manipulation processes effectively.
Table of Contents (20 chapters)
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2
Part 1: Guiding Tips and Principles
7
Part 2: Aggregations by Example
16
Afterword

Array fields joining

Joining documents between two collections is an aggregation topic you explored extensively in Chapter 7, Joining Data Examples. However, sometimes, you only need to efficiently join two fields within the same document where at least one of the fields is an array. Let's look at how you can achieve this.

Scenario

You are developing a new dating website using a database to hold the profiles of all registered users. For each user profile, you will persist a set of the user's specified hobbies, each with a description of how the user says they conduct their pursuit. Each user's profile also captures what they prefer to do depending on their mood (e.g., happy, sad, chilling, etc.). When you show the user profiles on the website to a person searching for a date, you want to describe how each candidate user conducts their hobbies for each mood to help the person spot their ideal match.

Populating the sample data

First, drop any old versions of...

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